Managing Machine Learning projects with Google Cloud (MMLPGC)

Managing Machine Learning projects with Google Cloud (MMLPGC)

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Duration

2 Days

Certified Instructor

Course Id

nextecGC830

Course Description

Course Overview

The Managing Machine Learning Projects with Google Cloud (MMLPGC) course is designed to provide individuals with the knowledge and skills needed to effectively manage machine learning (ML) projects using the Google Cloud Platform (GCP). This course covers the key aspects of ML project management, including project planning, data management, model development, deployment, and monitoring.

Prerequisites

To enroll in the MMLPGC course, participants should have a solid understanding of machine learning concepts and familiarity with GCP fundamentals. Prior experience with ML model development, data preprocessing, and Python programming will be beneficial. Participants should also have a basic understanding of cloud computing and GCP services.

Methodology

The MMLPGC course follows a blended learning approach, combining theoretical instruction, case studies, discussions, and hands-on labs. Participants will engage in instructor-led sessions where ML project management concepts and best practices are explained. They will also have access to GCP resources and tools to gain practical experience in managing ML projects. The course encourages active participation, discussions, and collaborative problem-solving to reinforce learning.

Course Outline

Introduction to Managing ML Projects on GCP

Overview of ML project lifecycle and key stakeholders

Understanding the role of project managers in ML projects

Overview of GCP tools and services for ML project management

Project Planning and Data Management

Defining ML project goals and success metrics

Gathering and preparing data for ML projects

Ensuring data quality and managing data pipelines

Model Development and Evaluation

Developing ML models using GCP’s ML tools (e.g., AutoML, AI Platform)

Training, tuning, and evaluating ML models

Interpreting model performance and metrics

Model Deployment and Serving

Deploying ML models using AI Platform Prediction or Cloud Functions

Managing model versions and rollouts

Ensuring model scalability, availability, and security

ML Project Monitoring and Maintenance

Establishing model performance monitoring mechanisms

Detecting and addressing model drift and bias

Managing model updates and retraining

ML Project Governance and Ethical Considerations

Ensuring compliance with data privacy and regulatory requirements

Addressing bias and fairness in ML models

Establishing responsible AI practices in ML projects

Outcome

By the end of the MMLPGC course, participants will have:

  • Developed a comprehensive understanding of ML project management principles and best practices
  • Acquired practical knowledge in planning, data management, model development, deployment, and monitoring
  • Gained expertise in leveraging GCP’s ML tools and services for project management
  • Learned ethical considerations and responsible AI practices in ML projects
  • Gained hands-on experience through practical labs and exercises
  • Prepared to effectively manage ML projects using GCP, ensuring successful deployment and ongoing maintenance

Labs

The MMLPGC course includes hands-on labs that provide participants with practical experience in managing ML projects on GCP. Some examples of lab exercises include:

  • Setting project goals and defining success metrics
  • Preparing and preprocessing data for ML projects
  • Developing and evaluating ML models using AutoML or AI Platform
  • Deploying ML models using AI Platform Prediction or Cloud Functions
  • Implementing model performance monitoring and detecting drift
  • Addressing ethical considerations and fairness in ML models

These labs enable participants to apply the concepts learned in the course and gain hands-on experience in managing ML projects using GCP’s tools and services, allowing them to develop practical skills in successfully executing ML projects from planning to deployment and monitoring.

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